Selecting resource configurations for query execution

ABSTRACT

The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.

RELATED APPLICATIONS

This application claims benefit of priority to U.S. ProvisionalApplication Ser. No. 62/382,477, entitled “Managed Query Service,” filedSep. 1, 2016, and which is incorporated herein by reference in itsentirety.

BACKGROUND

Computing systems for querying of large sets of data can be extremelydifficult to implement and maintain. In many scenarios, for example, itis necessary to first create and configure the infrastructure (e.g.server computers, storage devices, networking devices, etc.) to be usedfor the querying operations. It might then be necessary to performextract, transform, and load (“ETL”) operations to obtain data from asource system and place the data in data storage. It can also be complexand time consuming to install, configure, and maintain the databasemanagement system (“DBMS”) that performs the query operations. Moreover,many DBMS are not suitable for querying extremely large data sets in aperformant manner.

Computing clusters can be utilized in some scenarios to query large datasets in a performant manner. For instance, a computing cluster can havemany nodes that each execute a distributed query framework forperforming distributed querying of a large data set. Such computingclusters and distributed query frameworks are, however, also difficultto implement, configure, and maintain. Moreover, incorrect configurationand/or use of computing clusters such as these can result in thenon-optimal utilization of processor, storage, network and, potentially,other types of computing resources.

The disclosure made herein is presented with respect to these and otherconsiderations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a data flow diagram for selecting resourceconfigurations for query execution, according to some embodiments.

FIG. 2 is a logical block diagram illustrating a provider networkoffering a managed query service that selects resource configurationsfor executing queries, according to some embodiments.

FIG. 3 is a logical block diagram illustrating a managed query service,according to some embodiments.

FIG. 4 is a diagram illustrating interactions between clients andmanaged query service, according to some embodiments.

FIG. 5 is a sequence diagram for managed execution of queries utilizinga resource planner, according to some embodiments.

FIG. 6 is a logical block diagram illustrating a cluster processing aquery as part of managed query execution, according to some embodiments.

FIG. 7 is a logical block diagram illustrating a resource planner thatselects resource configurations for executing queries, according to someembodiments.

FIG. 8 is logical block diagram illustrating interactions between aresource management service and pools of resources, according to someembodiments.

FIG. 9 is a high-level flowchart illustrating various methods andtechniques to implement selecting resource configurations for queryexecution, according to some embodiments.

FIG. 10 is a high-level flowchart illustrating various methods andtechniques to implement routing queries to selected computing resources,according to some embodiments.

FIG. 11 is a high-level flowchart illustrating various methods andtechniques to apply a query execution model to select a resourceconfiguration, according to some embodiments.

FIG. 12 is a high-level flowchart illustrating various methods andtechniques to determine resource provisioning recommendations accordingto the execution of prior queries, according to some embodiments.

FIG. 13 is a logical block diagram that shows an illustrative operatingenvironment that includes a service provider network that can beconfigured to implement aspects of the functionality described herein,according to some embodiments.

FIG. 14 is a logical block diagram illustrating a configuration for adata center that can be utilized to implement aspects of thetechnologies disclosed herein, according to some embodiments.

FIG. 15 illustrates an example system configured to implement thevarious methods, techniques, and systems described herein, according tosome embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first contact could be termed asecond contact, and, similarly, a second contact could be termed a firstcontact, without departing from the scope of the present invention. Thefirst contact and the second contact are both contacts, but they are notthe same contact.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of selecting resource configurations for queryexecution are described herein. FIG. 1 illustrates a data flow diagramfor selecting resource configurations for query execution, according tosome embodiments. Configured computing resources 120 may beinstantiated, configured, and otherwise prepared for executing differenttypes of queries, such as query 102, in some embodiments. For example,configured computing resources may be one or more nodes, instances,hosts, or other collections of computing resources (e.g., a cluster ofcomputing resources) that implement a query engine (e.g., a distributedquery processing engine or framework) for executing queries with respectto data sets (e.g., that may be remotely stored), in one embodiment.Computing resources 120 may be differently configured, in at least someembodiments, providing opportunities to offer different executionperformance for queries. As different queries may be executed to providedifferent types of performance, resource selection 110 may beimplemented to intelligently route queries to resources that are likelyto provide a desired performance for executing the queries, in variousembodiments. Moreover, as different types of performance characteristicsfor queries may be achieved using different configurations of computingresources, query execution can be optimized on behalf of a client thatsubmitted the query or a service that manages the resources forexecuting queries, so that efficient utilization of resources can beachieved, in some embodiments.

For example, as illustrated in FIG. 1, resource selection 110 mayreceive a query 102. In order to select a resource for query 102,resource selection 110 may obtain available computing resources 122(e.g., those computing resources not executing another query) andresource configurations for the execution of prior queries 132 fromquery execution history 130. Based on the performance of prior querieson the resource configurations, an available computing resource 122 canbe selected that provides a similar configuration to one of the priorconfigurations to execute query 102. For example, if query 102 is to beexecuted within an execution limit (e.g., for execution time orexecution cost), then the resource configurations that executed priorqueries that achieved times or costs with the execution limitation maybe considered. As discussed below with regard to FIGS. 7, and 9-11,various kinds of models based on machine learning or other statisticalanalysis may be implemented to compare query 102 with the executionperformance of prior queries and prior resource configurations. Forexample, a feature set or other information about query 102 may bedetermined form query 102 and used to compare query 102 to the priorexecution of other queries.

A resource may be selected, as indicated at 140, that satisfies theexecution limitation (e.g., a resource selection goal), and may beinstructed to perform the query 112. For example, selected computingresource 140 may implement a cluster of nodes that apply a SQL query toone or multiple data sets stored in one or multiple locations in orderto generate query results 142. In addition to generating query results142, query execution data 116 may be collected and stored as part ofquery execution history 130, in order to update the resource selectiondata available for analysis at resource selection 110 with furtherexamples of resource configuration mapped to a performance outcome for aquery, as discussed below with regard to FIGS. 7 and 10.

Please note that the previous description of selecting resourceconfigurations for query execution is a logical illustration and thus isnot to be construed as limiting as to the implementation of resourceselection, computing resources, or query execution history.

This specification begins with a general description of a providernetwork that implements a resource management service that selectingresource configurations for query execution that are queries receivedfrom another network-based service, a managed query service. Thenvarious examples of the managed query service and resource managementservice (along with other services that may be utilized or implemented)including different components/modules, or arrangements ofcomponents/module that may be employed as part of implementing theservices are discussed. A number of different methods and techniques toimplement selecting resource configurations for query execution are thendiscussed, some of which are illustrated in accompanying flowcharts.Finally, a description of an example computing system upon which thevarious components, modules, systems, devices, and/or nodes may beimplemented is provided. Various examples are provided throughout thespecification.

FIG. 2 is a logical block diagram illustrating a provider networkoffering a managed query service that selects resource configurationsfor executing queries, according to some embodiments. Provider network200 may be a private or closed system or may be set up by an entity suchas a company or a public sector organization to provide one or moreservices (such as various types of cloud-based storage) accessible viathe Internet and/or other networks to clients 250, in some embodiments.Provider network 200 may be implemented in a single location or mayinclude numerous data centers hosting various resource pools, such ascollections of physical and/or virtualized computer servers, storagedevices, networking equipment and the like (e.g., FIGS. 13, 14 andcomputing system 2000 described below with regard to FIG. 15), needed toimplement and distribute the infrastructure and storage services offeredby the provider network 200. In some embodiments, provider network 200may implement various computing resources or services, such as a virtualcompute service 210, data processing service(s) 220, (e.g., relationalor non-relational (NoSQL) database query engines, map reduce processing,data flow processing, and/or other large scale data processingtechniques), data storage service(s) 230, (e.g., an object storageservice, block-based storage service, or data storage service that maystore different types of data for centralized access) other services 240(any other type of network based services (which may include variousother types of storage, processing, analysis, communication, eventhandling, visualization, and security services not illustrated), managedquery service 270, data catalog service 280, and resource managementservice 290.

In various embodiments, the components illustrated in FIG. 2 may beimplemented directly within computer hardware, as instructions directlyor indirectly executable by computer hardware (e.g., a microprocessor orcomputer system), or using a combination of these techniques. Forexample, the components of FIG. 2 may be implemented by a system thatincludes a number of computing nodes (or simply, nodes), each of whichmay be similar to the computer system embodiment illustrated in FIG. 15and described below. In various embodiments, the functionality of agiven system or service component (e.g., a component of data storageservice 230) may be implemented by a particular node or may bedistributed across several nodes. In some embodiments, a given node mayimplement the functionality of more than one service system component(e.g., more than one data store component).

Virtual compute service 210 may be implemented by provider network 200,in some embodiments. Virtual computing service 210 may offer instancesand according to various configurations for client(s) 250 operation. Avirtual compute instance may, for example, comprise one or more serverswith a specified computational capacity (which may be specified byindicating the type and number of CPUs, the main memory size, and so on)and a specified software stack (e.g., a particular version of anoperating system, which may in turn run on top of a hypervisor). Anumber of different types of computing devices may be used singly or incombination to implement the compute instances and of provider network200 in different embodiments, including general purpose or specialpurpose computer servers, storage devices, network devices and the like.In some embodiments instance client(s) 250 or other any other user maybe configured (and/or authorized) to direct network traffic to a computeinstance.

Compute instances may operate or implement a variety of differentplatforms, such as application server instances, Java™ virtual machines(JVMs), general purpose or special-purpose operating systems, platformsthat support various interpreted or compiled programming languages suchas Ruby, Perl, Python, C, C++ and the like, or high-performancecomputing platforms) suitable for performing client(s) 202 applications,without for example requiring the client(s) 250 to access an instance.Applications (or other software operated/implemented by a computeinstance and may be specified by client(s), such as custom and/oroff-the-shelf software.

In some embodiments, compute instances have different types orconfigurations based on expected uptime ratios. The uptime ratio of aparticular compute instance may be defined as the ratio of the amount oftime the instance is activated, to the total amount of time for whichthe instance is reserved. Uptime ratios may also be referred to asutilizations in some implementations. If a client expects to use acompute instance for a relatively small fraction of the time for whichthe instance is reserved (e.g., 30%-35% of a year-long reservation), theclient may decide to reserve the instance as a Low Uptime Ratioinstance, and pay a discounted hourly usage fee in accordance with theassociated pricing policy. If the client expects to have a steady-stateworkload that requires an instance to be up most of the time, the clientmay reserve a High Uptime Ratio instance and potentially pay an evenlower hourly usage fee, although in some embodiments the hourly fee maybe charged for the entire duration of the reservation, regardless of theactual number of hours of use, in accordance with pricing policy. Anoption for Medium Uptime Ratio instances, with a corresponding pricingpolicy, may be supported in some embodiments as well, where the upfrontcosts and the per-hour costs fall between the corresponding High UptimeRatio and Low Uptime Ratio costs.

Compute instance configurations may also include compute instances witha general or specific purpose, such as computational workloads forcompute intensive applications (e.g., high-traffic web applications, adserving, batch processing, video encoding, distributed analytics,high-energy physics, genome analysis, and computational fluid dynamics),graphics intensive workloads (e.g., game streaming, 3D applicationstreaming, server-side graphics workloads, rendering, financialmodeling, and engineering design), memory intensive workloads (e.g.,high performance databases, distributed memory caches, in-memoryanalytics, genome assembly and analysis), and storage optimizedworkloads (e.g., data warehousing and cluster file systems). Size ofcompute instances, such as a particular number of virtual CPU cores,memory, cache, storage, as well as any other performance characteristic.Configurations of compute instances may also include their location, ina particular data center, availability zone, geographic, location, etc.. . . and (in the case of reserved compute instances) reservation termlength. Different configurations of compute instances, as discussedbelow with regard to FIG. 3, may be implemented as computing resourcesassociated in different pools of resources managed by resourcemanagement service 290 for executing jobs routed to the resources, suchas queries routed to select resources by managed query service 270.

Data processing services 220 may be various types of data processingservices to perform different functions (e.g., query or other processingengines to perform functions such as anomaly detection, machinelearning, data lookup, or any other type of data processing operation).For example, in at least some embodiments, data processing services 230may include a map reduce service that creates clusters of processingnodes that implement map reduce functionality over data stored in one ofdata storage services 240. Various other distributed processingarchitectures and techniques may be implemented by data processingservices 230 (e.g., grid computing, sharding, distributed hashing,etc.). Note that in some embodiments, data processing operations may beimplemented as part of data storage service(s) 230 (e.g., query enginesprocessing requests for specified data). Data processing service(s) 230may be clients of data catalog service 220 in order to obtain structuralinformation for performing various processing operations with respect todata sets stored in data storage service(s) 230, as provisionedresources in a pool for managed query service 270.

Data catalog service 280 may provide a catalog service that ingests,locates, and identifies data and the schema of data stored on behalf ofclients in provider network 200 in data storage services 230. Forexample, a data set stored in a non-relational format may be identifiedalong with a container or group in an object-based data store thatstores the data set along with other data objects on behalf of a samecustomer or client of provider network 200. In at least someembodiments, data catalog service 280 may direct the transformation ofdata ingested in one data format into another data format. For example,data may be ingested into data storage service 230 as single file orsemi-structured set of data (e.g., JavaScript Object Notation (JSON)).Data catalog service 280 may identify the data format, structure, or anyother schema information of the single file or semi-structured set ofdata. In at least some embodiments, the data stored in another dataformat may be converted to a different data format as part of abackground operation (e.g., to discover the data type, column types,names, delimiters of fields, and/or any other information to constructthe table of semi-structured data in order to create a structuredversion of the data set). Data catalog service 280 may then make theschema information for data available to other services, computingdevices, or resources, such as computing resources or clustersconfigured to process queries with respect to the data, as discussedbelow with regard to FIGS. 3-6.

Data storage service(s) 230 may implement different types of data storesfor storing, accessing, and managing data on behalf of clients 250 as anetwork-based service that enables clients 250 to operate a data storagesystem in a cloud or network computing environment. For example, datastorage service(s) 230 may include various types of database storageservices (both relational and non-relational) for storing, querying, andupdating data. Such services may be enterprise-class database systemsthat are highly scalable and extensible. Queries may be directed to adatabase in data storage service(s) 230 that is distributed acrossmultiple physical resources, and the database system may be scaled up ordown on an as needed basis. The database system may work effectivelywith database schemas of various types and/or organizations, indifferent embodiments. In some embodiments, clients/subscribers maysubmit queries in a number of ways, e.g., interactively via an SQLinterface to the database system. In other embodiments, externalapplications and programs may submit queries using Open DatabaseConnectivity (ODBC) and/or Java Database Connectivity (JDBC) driverinterfaces to the database system.

One data storage service 230 may be implemented as a centralized datastore so that other data storage services may access data stored in thecentralized data store for processing and or storing within the otherdata storage services, in some embodiments. A may provide storage andaccess to various kinds of object or file data stores for putting,updating, and getting various types, sizes, or collections of dataobjects or files. Such data storage service(s) 230 may be accessed viaprogrammatic interfaces (e.g., APIs) or graphical user interfaces. Acentralized data store may provide virtual block-based storage formaintaining data as part of data volumes that can be mounted or accessedsimilar to local block-based storage devices (e.g., hard disk drives,solid state drives, etc.) and may be accessed utilizing block-based datastorage protocols or interfaces, such as internet small computerinterface (iSCSI).

In at least some embodiments, one of data storage service(s) 230 may bea data warehouse service that utilizes a centralized data storeimplemented as part of another data storage service 230. A datawarehouse service as may offer clients a variety of different datamanagement services, according to their various needs. In some cases,clients may wish to store and maintain large of amounts data, such assales records marketing, management reporting, business processmanagement, budget forecasting, financial reporting, website analytics,or many other types or kinds of data. A client's use for the data mayalso affect the configuration of the data management system used tostore the data. For instance, for certain types of data analysis andother operations, such as those that aggregate large sets of data fromsmall numbers of columns within each row, a columnar database table mayprovide more efficient performance. In other words, column informationfrom database tables may be stored into data blocks on disk, rather thanstoring entire rows of columns in each data block (as in traditionaldatabase schemes).

Managed query service 270, as discussed below in more detail with regardto FIGS. 3-7, may manage the execution of queries on behalf of clientsso that clients may perform queries over data stored in one or multiplelocations (e.g., in different data storage services, such as an objectstore and a database service) without configuring the resources toexecute the queries, in various embodiments. Resource management service290, as discussed in more detail below, may manage and provide pools ofcomputing resources for different services like managed query service270 in order to execute jobs on behalf the different services, asdiscussed above with regard to FIG. 1.

Generally speaking, clients 250 may encompass any type of clientconfigurable to submit network-based requests to provider network 200via network 260, including requests for storage services (e.g., arequest to create, read, write, obtain, or modify data in data storageservice(s) 240, etc.) or managed query service 270 (e.g., a request toquery data in a data set stored in data storage service(s) 230). Forexample, a given client 250 may include a suitable version of a webbrowser, or may include a plug-in module or other type of code modulethat may execute as an extension to or within an execution environmentprovided by a web browser. Alternatively, a client 250 may encompass anapplication such as a database application (or user interface thereof),a media application, an office application or any other application thatmay make use of storage resources in data storage service(s) 240 tostore and/or access the data to implement various applications. In someembodiments, such an application may include sufficient protocol support(e.g., for a suitable version of Hypertext Transfer Protocol (HTTP)) forgenerating and processing network-based services requests withoutnecessarily implementing full browser support for all types ofnetwork-based data. That is, client 250 may be an application mayinteract directly with provider network 200. In some embodiments, client250 may generate network-based services requests according to aRepresentational State Transfer (REST)-style network-based servicesarchitecture, a document- or message-based network-based servicesarchitecture, or another suitable network-based services architecture.

In some embodiments, a client 250 may provide access to provider network200 to other applications in a manner that is transparent to thoseapplications. For example, client 250 may integrate with an operatingsystem or file system to provide storage on one of data storageservice(s) 240 (e.g., a block-based storage service). However, theoperating system or file system may present a different storageinterface to applications, such as a conventional file system hierarchyof files, directories and/or folders. In such an embodiment,applications may not need to be modified to make use of the storagesystem service model. Instead, the details of interfacing to the datastorage service(s) 240 may be coordinated by client 250 and theoperating system or file system on behalf of applications executingwithin the operating system environment.

Clients 250 may convey network-based services requests (e.g., accessrequests directed to data in data storage service(s) 240, operations,tasks, or jobs, being performed as part of data processing service(s)230, or to interact with data catalog service 220) to and receiveresponses from provider network 200 via network 260. In variousembodiments, network 260 may encompass any suitable combination ofnetworking hardware and protocols necessary to establishnetwork-based-based communications between clients 250 and providernetwork 200. For example, network 260 may generally encompass thevarious telecommunications networks and service providers thatcollectively implement the Internet. Network 260 may also includeprivate networks such as local area networks (LANs) or wide areanetworks (WANs) as well as public or private wireless networks. Forexample, both a given client 250 and provider network 200 may berespectively provisioned within enterprises having their own internalnetworks. In such an embodiment, network 260 may include the hardware(e.g., modems, routers, switches, load balancers, proxy servers, etc.)and software (e.g., protocol stacks, accounting software,firewall/security software, etc.) necessary to establish a networkinglink between given client 250 and the Internet as well as between theInternet and provider network 200. It is noted that in some embodiments,clients 250 may communicate with provider network 200 using a privatenetwork rather than the public Internet.

FIG. 3 is a logical block diagram illustrating a managed query service,according to some embodiments. As discussed below with regard to FIGS.4-9, managed query service 270 may leverage the capabilities of variousother services in provider network 200. For example, managed queryservice 270 may utilize resource management service 290 to provision andmanage pools of preconfigured resources to execute queries, provideresources of preconfigured queries, and return utilized resources toavailability. For example, resource management service 290 mayinstantiate, configure, and provide resource pool(s) 350 a and 350 nthat include pool resource(s) 352 a and 352 n from one or more differentresource services, such as computing resource(s) 354 in virtual computeservice 210 and computing resource(s) 356 in data processing service(s)220. Resource management service 290 may send requests to create,configure, tag (or otherwise associate) resources 352 for a particularresource pool, terminate, reboot, otherwise operate resources 352 inorder to execute jobs on behalf of other network-based services.

Once a resource from a pool is provided (e.g., by receiving anidentifier or other indicator of the resource to utilize), managed queryservice 270 may interact directly with the resource 354 in virtualcompute service 210 or the resource 356 in data processing services 220to execute queries, in various embodiments. Managed query service 270may utilize data catalog service 280, in some embodiments to store dataset schemas 352, as discussed below with regard to FIG. 4, forsubsequent use when processing queries, as discussed below with regardto FIGS. 5-7, in some embodiments. For example, a data set schema mayidentify the field or column data types of a table as part of a tabledefinition so that a query engine (executing on a computing resource),may be able to understand the data being queried, in some embodiments.Managed query service 270 may also interact with data storage service(s)230 to directly source data sets 370 or retrieve query results 380, insome embodiments.

Managed query service 270 may implement a managed query interface 310 tohandle requests from different client interfaces, as discussed belowwith regard to FIG. 4. For example, different types of requests, such asrequests formatted according to an Application Programmer Interface(API), standard query protocol or connection, or requests received via ahosted graphical user interface implemented as part of managed queryservice may be handled by managed query interface 310.

Managed query service 270 may implement managed query service controlplane 320 to manage the operation of service resources (e.g., requestdispatchers for managed query interface 310, resource planner workersfor resource planner 330, or query tracker monitors for query tracker340). Managed query service control plane 320 may direct requests toappropriate components as discussed below with regard to FIGS. 5 and 6.Managed query service 270 may implement authentication and authorizationcontrols for handling requests received via managed query interface 310.For example, managed query service control plane 320 may validate theidentity or authority of a client to access the data set identified in aquery received from a client (e.g., by validating an access credential).In at least some embodiments, managed query service control plane 320may maintain (in an internal data store or as part of a data set in anexternal data store, such as in one of data storage service(s) 230),query history, favorite queries, or query execution logs, and othermanaged query service historical data. Query execution costs may bebilled, calculated or reported by managed query service control plane320 to a billing service (not illustrated) or other system for reportingusage to users of managed query service, in some embodiments.

Managed query service 270 may implement resource planner 330 tointelligently select available computing resources from pools forexecution of queries, in some embodiments, as discussed in more detailbelow with regard to FIG. 7. For example, resource planner 330 mayevaluated collected data statistics associated with query execution(e.g., reported by computing resources) and determine an estimatednumber or configuration of computing resources for executing a querywithin some set of parameters (e.g., cost, time, etc.). For example,machine learning techniques may be applied by resource planner 330 togenerate a query estimation model that can be applied to the features ofa received query to determine the number/configuration of resources, inone embodiment. Resource planner 330 may then provide or identify whichones of the resources available to execute the query from a pool thatmay best fit the estimated number/configuration, in one embodiment.

In various embodiments, managed query service 270 may implement querytracker 340 in order to manage the execution of queries at computeclusters, track the status of queries, and obtain the resources for theexecution of queries from resource management service 290. For example,query tracker 340 may maintain a database or other set of trackinginformation based on updates received from different managed queryservice agents implemented on provisioned computing resources (e.g.,computing clusters as discussed below with regard to FIGS. 5-6).

FIG. 4 is a diagram illustrating interactions between clients andmanaged query service, according to some embodiments. Client(s) 400 maybe client(s) 250 in FIG. 2 above or other clients (e.g., other servicessystems or components implemented as part of provider network 200 or aspart of an external service, system, or component, such as dataexploration or visualization tools (e.g., Tableau, Looker,MicroStrategy, Qliktech, or Spotfire). Clients 400 can send variousrequests to managed query service 270 via managed query interface 310.Managed query interface 310 may offer a management console 440, whichmay provider a user interface to submit queries 442 (e.g., graphical orcommand line user interfaces) or register data schemas 444 for executingqueries. For example, management console 440 may be implemented as partof a network-based site (e.g., an Internet website for provider network200) that provides various graphical user interface elements (e.g., textediting windows, drop-down menus, buttons, wizards or workflows) tosubmit queries or register data schemas. Managed query interface 310 mayimplement programmatic interfaces 410 (e.g., various ApplicationProgramming Interface (API) commands) to perform queries, and variousother illustrated requests. In some embodiments, managed query interface310 may implement custom drivers that support standard communicationprotocols for querying data, such as JDBC driver 430 or ODBC driver 420.

Clients 400 can submit many different types of request to managed queryinterface 310. For example, in one embodiment, clients 400 can submitrequests 450 to create, read, modify, or delete data schemas. Forexample, a new table schema can be submitted via a request 450. Request450 may include a name of the data set (e.g., table), a location of thedata set (e.g. an object identifier in an object storage service, suchas data storage service 230, file path, uniform resource locator, orother location indicator), number of columns, column names, data typesfor fields or columns (e.g., string, integer, Boolean, timestamp, array,map, custom data types, or compound data types), data format (e.g.,formats including, but not limited to, JSON, CSV, AVRO, ORC, PARQUET,tab delimited, comma separated, as well as custom or standardserializers/desrializers), partitions of a data set (e.g., according totime, geographic location, or other dimensions), or any other schemainformation for process queries with respect to data sets, in variousembodiments. In at least some embodiments, request tocreate/read/modify/delete data set schemas may be performed using a datadefinition language (DDL), such as Hive Query Language (HQL). Managedquery interface 310 may perform respective API calls or other requests452 with respect to data catalog service 280, to store the schema forthe data set (e.g., as part of table schemas 402). Table schemas 402 maybe stored in different formats (e.g., Apache Hive). Note, in otherembodiments, managed query service 270 may implement its own metadatastore.

Clients 400 may also send queries 460 and query status 470 requests tomanaged query interface 310 which may direct those requests 460 and 470to managed query service control plane 320, in various embodiments, asdiscussed below with regard to FIGS. 5 and 6. Queries 460 may beformatted according to various types of query languages, such asStructured Query Language (SQL) or HQL.

Client(s) 400 may also submit requests for query history 480 or otheraccount related query information (e.g., favorite or common queries)which managed query. In some embodiments, client(s) 400 mayprogrammatically trigger the performance of past queries by sending arequest to execute a saved query 490, which managed query servicecontrol plane 320 may look-up and execute. For example, execute savedquery request may include a pointer or other identifier to a querystored or saved for a particular user account or client. Managed queryservice control plane 320 may then access that user query store toretrieve and execute the query (according to techniques discussed belowwith regard to FIGS. 5-6).

FIG. 5 is a sequence diagram for managed execution of queries utilizinga resource planner, according to some embodiments. Query 530 may bereceived at managed query service control plane 320 which may submit thequery 532 to resource planner 340. Resource planner 340 may analyze thequery to determine the optimal cluster to process the query based onhistorical data for processing queries and available cluster(s) 534received from resource management service 290. Resource planner 340 maythen select a query and submit the query to query tracker 340 indicatingthe selected cluster 536 for execution. Query tracker 340 may theninitiate execution of the query 538 at the provisioned cluster 510,sending a query execution instruction to a managed query agent 512.

Managed query agent 512 may get schema 540 for the data sets(s) 520 fromdata catalog service 280, which may return the appropriate schema 542.Provisioned cluster 510 can then generate a query execution plan andexecute the query 544 with respect to data set(s) 520 according to thequery plan. Managed query agent 512 may send query status 546 to querytracker 340 which may report query status 548 in response to get querystatus 546 request, sending a response 550 indicating the query status550. Provisioned cluster 510 may store the query results 552 in a resultstore 522 (which may be a data storage service 230). Managed queryservice control plane 320 may receive q request to get a query results554 and get query results 556 from results store 522 and provide thequery results 558 in response, in some embodiments.

Different types of computing resources may be provisioned and configuredin resource pools, in some embodiments. Single-node clusters ormulti-node compute clusters may be one example of a type of computingresource provisioned and configured in resource pools by resourcemanagement service 290 to service queries for managed query service 270.FIG. 6 is a logical block diagram illustrating a cluster processing aquery as part of managed query execution, according to some embodiments.Cluster 610 may implement a computing node 620 that is a leader node(according to the query engine 624 implemented by cluster 610). In someembodiments, no single node may be a leader node, or the leader node mayrotate from processing one query to the next. Managed query agent 622may be implemented as part of leader node 620 in order to provide aninterface between the provisioned resource, cluster 610, and othercomponents of managed query service 270 and resource management service290. For example, managed query agent 622 may provide further data tomanaged query service 270, such as the status 608 of the query (e.g.executing, performing I/O, performing aggregation, etc.) and executionmetrics 606 (e.g., health metrics, resource utilization metrics, costmetrics, length of time, etc.). In some embodiments, managed query agent622 may provide cluster/query status 608 and execution metric(s) 606 toresource management service 290 (in order to make pool managementdecisions, such as modification events, lease requests, etc.). Forexample, managed query agent 622 may indicate cluster status 608 toresource management service 290 indicating that a query has completedand that the cluster 610 is ready for reassignment (or other resourcelifecycle operations).

Leader node 620 may implement query engine 624 to execute queries, suchas query 602 which may be received via managed query agent 622 as query603. For instance, managed query agent may implement a programmaticinterface for query tracker to submit queries (as discussed above inFIGS. 5 and 6), and then generate and send the appropriate queryexecution instruction to query engine 624. Query engine 624 may generatea query execution plan for received queries 603. In at least someembodiments, leader node 620, may obtain schema information for the dataset(s) 670 from the data catalog service 280 or metadata stores for data662 (e.g., data dictionaries, other metadata stores, other dataprocessing services, such as database systems, that maintain schemainformation) for data 662, in order to incorporate the schema data intothe generation of the query plan and the execution of the query. Leadernode 620 may generate and send query execution instructions 640 tocomputing nodes that access and apply the query to data 662 in datastore(s) 660. Compute nodes, such as nodes 630 a, 630 b, and 630 n, mayrespectively implement query engines 632 a, 632 b, and 632 n to executethe query instructions, apply the query to the data 650, and returnpartial results 640 to leader node 620, which in turn may generate andsend query results 604. Query engine 624 and query engines 632 mayimplement various kinds of distributed query or data processingframeworks, such as the open source Presto distributed query frameworkor the Apache Spark framework.

FIG. 7 is a logical block diagram illustrating a resource planner thatselects resource configurations for executing queries, according to someembodiments. Resource planner 330, as discussed above, may determine orselect a resource configuration for the execution of a query. Asillustrated in FIG. 7, a query 702 may be received at resource planner330. Resource planner may implement one or more query model(s) 720 toevaluate the query 702. Query model(s) 720 may be generated usingvarious types of machine learning and other statistical analyses. Forexample, resource planner 330 may implement query model training 750 toreceive information for inclusion in the query model(s) 720 and updatethe query model(s) 754, providing supervised learning to adjust themodel to map different types of queries and resource configurations withdifferent outcomes. In this way, query model(s) 720 can classify orotherwise identify features to be compared with received queries 702 inorder to determine a configuration for executing the query according toa received execution limitation 704.

Query model(s) 720 can be generated using many different sources ofinformation. For example, as illustrated in FIG. 7, query model training750 may include table metadata 752 as part of training and updatingquery model(s) 720, in some embodiments. Table metadata may includeinformation describing tables or other data evaluated or searched byqueries, such as the number of rows in a table, the number of distinctvalues in a column, the number of null values in a column, thedistribution of data values in a column (e.g., according to ahistogram), the cardinality of data values in a column, size or numberof data blocks in a table, index statistics, and the like. In at leastsome embodiments, table metadata 752 may be obtained from data catalogservice 280 and obtained via API or other requests to data catalogservice to receive the table metadata 752.

Query model(s) 720 may also be generated using execution data 742received from the execution of queries at computing resources ofdifferent configurations, in some embodiments. For example, the memoryconsumed, processing capacity consumed, number of slots, nodes, or otherexecution units consumed/utilized, execution plans for prior queries(e.g., including the various types of operations selected by the plan toperform the prior queries), the total execution time to perform thequery, a resource or determined cost (e.g., in terms of work or costunits, such as IOPS or monetary units), failure or success indications,failure types (e.g., including error codes or exceptions generated).

Updates to query model(s) 754 may be periodically performed (e.g., dailyor weekly) or in response to trigger events (e.g., number of queriesprocessed since last update, number of new queries processed since lastupdate, new set of execution data 742 or table metadata 754 receivedetc.), in some embodiments. Query model training 750 may apply differenttypes of machine learning techniques to generate and update querymodel(s) 720. For instance, the information related to a prior query(e.g., execution metadata, including the query execution plan, executioncosts, etc., and table metadata, including number of rows in accesstables) may be used to generate feature vectors that create a featurespace for performing comparisons with newly received queries, in oneembodiment. Feature extraction or selection techniques may beimplemented to determine which data (e.g., what kinds of table metadataor execution data) are more determinative for different performanceoutcomes for a query in order to adjust the query model(s), in someembodiments. Note that although supervised learning techniques aredescribed above, in some embodiments, query model(s) 720 may begenerated using unsupervised learning techniques.

Query model(s) 720 may be applied to received queries in order todetermine a resource configuration, such as a query engine configurationfor a received query. For example, query 702 may be received at resourceplanner 330. In some embodiments, resource planner 330 may implementdifferent query plan generator(s) for query engine(s) 710. Differenttypes of query engines may be implemented to execute queries, and thus,plan generators for the different possible query engines may beimplemented in order to generate an execution plan for each queryengine. For example, a query engine utilizing the Presto framework maygenerate a different query execution plan than a query engine utilizingthe Apache Spark framework. Similarly, different query model(s) 720 maybe maintained for the different query engines, in some embodiments.Query plans may include various kinds operations that may be performedto execute a query, such as different types of join operations (e.g.,hash joins, distributed joins, etc.), different types of scanoperations, aggregation operations, predicate filters, and the like.

The query and query execution plans may be provided 712 and evaluatedusing query model(s) 720. For example, a feature vector for the querybased on the query execution plan and execution limitation 702 may begenerated so that the feature vector can then be compared or classified(e.g., using a linear function that assigns a score for each possibleconfiguration), in one embodiment. Scores, or other configurationinformation, may be determined for each query model in embodimentsimplementing multiple query model(s). The resulting classifications mayinclude a number of nodes, slots, containers, or other components for acomputing resource (e.g., in a cluster) as well as the configuration of(e.g., settings enabled) for a query engine. For example, differentquery engines may provide many different kinds of configuration oroptimization settings that can be enabled or disabled, in someembodiments. As part of applying the query model(s) 720, for instance,different configurations of a Presto framework may be determined (e.g.,configurations that enable or disable features such as memoryconfiguration properties, log file location properties, workerconfiguration properties, coordinator configuration properties, catalogproperties, Java Virtual Machine (JVM) properties, optimizer properties,node scheduler properties, exchange properties, distributed joins,distributed index joins, redistributed writes, session properties,etc.).

Application of query model(s) 720 may classify or otherwise indicate aresource configuration (e.g., query engine configuration(s) 722 for eachquery model that satisfies the execution limitation 704. Executionlimitations 702 may be time limitations (e.g., query execution timelimits), cost-defined limitations (e.g., number of resource units orcost units consumed to execute the query, service level agreements(SLAs), performance models, cost models, or any other limitation thatmay be placed on the execution of the query (e.g. a limitation excludingcertain query execution engines from consideration). In someembodiments, resource selection 730 may first select one query engineconfiguration to use (e.g., based on confidence scores or other valuesthat indicate the strength of the classification of the query in thedifferent query model(s) 720 that satisfies the execution limitation 704if multiple query model(s) are used.

Resource selection 730 may obtain a set (e.g., a snapshot of) availableresource(s) 734 for executing the query from resource manager service.Note that the available resource(s) 734 may be provided to queryselection engine 730 prior to the receipt of query 702, in someembodiments. Query selection engine 730 may then compare the availableresource(s) 734 with the query engine configuration(s) 722 to select theresource to execute the query. For example, resource selection 730 maycompare the number nodes in a cluster to see if the number of nodesmeets or exceeds the number of nodes identified in query engineconfiguration(s) 722, in one embodiment. Similarly, query selectionengine 730 may compare the configuration(s) of the different resources,such as the engine type (e.g., Presto, Apache Spark, etc.) and theconfiguration of the engine (e.g., which properties are enabled ordisabled on the engine). In some embodiments, resource selection mayselect a candidate list or set of query engines that meet or exceed thequery engine configuration(s) 722. For example, if the query engineconfiguration 722 identifies a cluster of 10 nodes, then 5 clusters with10 or greater nodes may be identified as a candidate list of resources.Resource selection 730 may then provide a selected resource 732 to querytracker 340 in order to execute the query. If the selected resource isno longer available (e.g., due to failure or having already beenassigned a query in the time between indicated as available 734 andselected 732), then another resource from the candidate set may beselected 732 (or the candidate set may be provided to query tracker 340,in some embodiments, for retry until assignment).

In some embodiments, query concurrency patterns 744 may alter or modifythe selection of resources. For instance, as noted above a same client,user, customer, or submitter may submit multiple queries which may berouted to the same resource so that the queries can be executed withoutrecycling or scrubbing the resource (as the queries would not beexecuted on resources for which the submitter of the query had not rightto access the data or results of the other queries executing at thatresource). Query concurrency patterns 744 may indicate the likelihoodthat a same submitter will submit another query in addition to query704, as well as the type of query the subsequent query may be, in someembodiments. In some embodiments, resource selection 730 may select aquery engine that can include capacity to accommodate both the receivedquery within the execution limitation 704 as well as the likelysubsequent query (which may also have an execution limitation). In thisway, resource selection 730 may still allow for queries to be co-locatedon resources executing queries from the same submitter.

In at least some embodiments, resource planner 330 may implementresource provisioning model 740. Resource provisioning model 740 may bea model that is generated from execution data 742 for previouslyexecuted queries to determine or recommend resources (including theconfiguration of the resources) for provisioning, in some embodiments.For example, resource provisioning model 740 may be generated fromvarious unsupervised learning techniques, such as clustering analysis,dimensionality reduction, and other such techniques to determine whichresource configurations (e.g., query engine types, query engineconfigurations, number of nodes, slots, or units) are likely to beutilized by queries (e.g., according to the techniques for selectionresources at resource selection 730). For example, clustering analysismay be performed to identify that ninety percent of clusters have queryengine type A, configuration settings B set, and have C number of nodes.Thus, resource provisioning model 746 may provide a recommendation 746to provision more resources with query engine type A, configurationsettings B set, and have C number of nodes. In some embodiments,resource provisioning model 740 may provide general provisioningrecommendations according to a set of classifications (e.g., smallclusters, medium clusters, larger clusters with engine type A, andconfiguration settings B set). In at least some embodiments, multipleresource provisioning models 740 may be implemented (e.g., to predictthe demand of different types of query engines for different times whenmaking provisioning recommendations).

FIG. 8 is logical block diagram illustrating interactions between aresource management service and pools of resources, according to someembodiments. Resource management service 290 may implement aprogrammatic interface (e.g., API) or other interface that allows othernetwork-based services (or a client or a provider network) to submitrequests for preconfigured resources from a resource pool managed byresource management service 290. For example, a request for availableclusters 830 may be received (e.g., from resource planner 330) in orderto provide a snapshot or other state of configured computing clusters820 in warm cluster pools 810. As discussed above with regard to FIGS. 5and 7, resource planner may then provide a selected cluster for thequery tracker to use for a received query. Query tracker may send anindication that identifies the selected cluster 840 (e.g., by specifyinga location, identifier, or other information for the identifiedcomputing resource) so that resource manager service 290 may remove theresource from the pool of available resource. For example, resourcemanagement service 290 may update state information for the cluster toindicate that the cluster is leased or otherwise unavailable. Resourcemanagement service 290 may also receive requests to release a cluster850 from a current assignment (e.g., as the query execution at thecluster is complete). Resource management service 290 may then updatestate information (e.g., the lease) for the cluster and pool to returnthe cluster to the pool, in some embodiments.

As indicated at 862, resource management service 290 may automatically(or in response to requests (not illustrated)), commission ordecommission pool(s) of clusters 810. For example in some embodiments,resource management service 290 may perform techniques that select thenumber and size of computing clusters 820 for the warm cluster pool 810.The number and size of the computing clusters 820 in the warm clusterpool 810 can be determined based upon a variety of factors including,but not limited to, historical and/or expected volumes of queryrequests, the price of the computing resources utilized to implement thecomputing clusters 820, and/or other factors or considerations, in someembodiments.

Once the number and size of computing clusters 820 has been determined,the computing clusters 820 may be instantiated, such as through the useof an on-demand computing service, or virtual compute service or dataprocessing service as discussed above in FIG. 2. The instantiatedcomputing clusters 820 can then be configured to process queries priorto receiving the queries at the managed query service. For example, andwithout limitation, one or more distributed query frameworks or otherquery processing engines can be installed on the computing nodes in eachof the computing clusters 820. As discussed above, in one particularimplementation, the distributed query framework may be the open sourcePRESTO distributed query framework. Other distributed query frameworkscan be utilized in other configurations. Additionally, distributedprocessing frameworks or other query engines can also be installed onthe host computers in each computing cluster 820. As discussed above,the distributed processing frameworks can be utilized in a similarfashion to the distributed query frameworks. For instance, in oneparticular configuration, the APACHE SPARK distributed processingframework can also, or alternately, be installed on the host computersin the computing clusters 820.

Instantiated and configured computing clusters 820 that are availablefor use by the managed query service 270 are added to the warm clusterpool 810, in some embodiments. A determination can be made as to whetherthe number or size of the computing clusters 820 in the warm clusterpool needs is to be adjusted, in various embodiments. The performance ofthe computing clusters 820 in the warm cluster pool 810 can be monitoredbased on metric(s) 890 received from the cluster pool. The number ofcomputing clusters 820 assigned to the warm cluster pool 810 and thesize of each computing cluster 820 (i.e. the number of host computers ineach computing cluster 820) in the warm cluster pool 810 can then beadjusted. Such techniques can be repeatedly performed in order tocontinually optimize the number and size of the computing clusters 820in the warm cluster pool 810. Configurations of clusters for a resourcepool or a new pool may be provided as provisioning recommendations (asdiscussed above with regard to FIG. 7), which may indicate theconfiguration of a cluster (e.g. query engine type, query engineconfiguration settings,

As indicated at 880, in some embodiments, resource management service270 may scrub clusters(s) 880, (e.g., as a result of the lease statetransitioning to expired or terminated) by causing the cluster toperform operations (e.g., a reboot, disk wipe, memory purge/dump, etc.)so that the cluster no longer retains client data and is ready toprocess another query. For example, resource management service 290 maydetermine whether a computing cluster 820 is inactive (e.g. thecomputing cluster 820 has not received a query in a predetermined amountof time). If resource management service 290 determines that thecomputing cluster 820 is inactive, then the computing cluster 820 may bedisassociated from the submitter of the query. The computing cluster 820may then be “scrubbed,” such as by removing data associated with thesubmitter of the queries from memory (e.g. main memory or a cache) ormass storage device (e.g. disk or solid state storage device) utilizedby the host computers in the computing cluster 820. The computingcluster 820 may then be returned to the warm cluster pool 810 for use inprocessing other queries. In some embodiments, some clusters that areinactive might not be disassociated from certain users in certainscenarios. In these scenarios, the user may have a dedicated warm poolof clusters 810 available for their use.

As indicated at 860, in some embodiments, resource management service290 may receive requests to configure resources or a pool of resources.For example, a request to configure a pool of resources may identify atype or size of cluster, a processing engine, machine image, or softwareto execute for individual clusters in the pool. In some embodiments, therequest may indicate a maximum number of resources in the pool, aminimum number of idle resources in the pool, and a maximum number ofidle resources in the pool. As indicated at 870, resource managementservice may receive a request to configure or specify a poolmodification event for a pool, in some embodiments. For example, thepool modification event may be defined according to one or morecriteria, such as the minimum number of idle resources, maximum numberof idle resources, average job execution time thresholds, pool orresource lifecycle/state conditions, or any other set of one or morecriteria that may be evaluated to detect a pool modification event.

Although FIGS. 2-8 have been described and illustrated in the context ofa provider network leveraging multiple different services to implementresource management service to select resource configurations for queryexecution, the various components illustrated and described in FIGS. 2-8may be easily applied to other systems, or devices that manage or selectresources for query execution from pools of configured resources. Assuch, FIGS. 2-8 are not intended to be limiting as to other embodimentsof a system that may implement stateful management of resource pools forexecuting jobs. FIG. 9 is a high-level flowchart illustrating variousmethods and techniques to implement selecting resource configurationsfor query execution, according to some embodiments. Various differentsystems and devices may implement the various methods and techniquesdescribed below, either singly or working together. For example, aresource management service as described above with regard to FIGS. 2-8may implement the various methods. Alternatively, a combination ofdifferent systems and devices may implement these methods. Therefore,the above examples and or any other systems or devices referenced asperforming the illustrated method, are not intended to be limiting as toother different components, modules, systems, or configurations ofsystems and devices.

As indicated at 910, a first query directed to data set(s) may bereceived, in various embodiments. The first query may be received thatis directed to data set(s) separately stored in remote data stores, invarious embodiments. For example, a query may be received via thevarious types of interfaces described above with regard to FIG. 4(programmatic, user console, driver, etc.), in one embodiment. A querymay be formatted according to different query languages, orspecifications of query languages including Structured Query Language(SQL) and/Hive Query Language (HQL). The query may include executionhints, specifying the type of query execution engine to utilize, queryexecution limits, or other parameters or properties for configuring theexecution of the query, in some embodiments.

As indicated at 920, computing resource(s) may be selected to executethe first query based, at least in part, on how the computingresource(s) are configured compared to computing resources used inperforming prior queries, in some embodiments. For example, a historicalquery model (e.g., such as a model generated according to machinelearning techniques) may be maintained that models the performance ofqueries with different characteristics based on different executionoutcomes (e.g., time to complete, cost to complete, resources consumedto complete, probability of failure or timeout, etc.). The historicalquery model may then be applied, in some embodiments to the query, bycomparing features of the query with respect to the features of queriesidentified in the model, as discussed in detail below with regard toFIG. 11. In some embodiments, a best performance outcome (e.g., a besteffort model or service level agreement) may be a default feature thatdetermines the selection of computing resources. In other embodiments, abest cost outcome (e.g., a lowest cost model or limitation) may be adefault features that determines the selection of the computingresources. In some embodiments, a client that submits the query caninclude in the query a hint or other indicator identifying theperformance outcome or limitation desired for processing of the query.

As indicated at 930, the first query may be performed at the selectedcomputing resource(s) with respect to the data set(s), in variousembodiments. For example, the resource request may be routed to theselected resource in some embodiments. A request to initiate or beingprocessing at the selected computing resource(s) may be performed, insome embodiments, according to an API request or the first query may beinitiated by transmitting the query in its original format to thecomputing resources for execution.

FIG. 10 is a high-level flowchart illustrating various methods andtechniques to implement routing queries to selected computing resources,according to some embodiments. As indicated at 1010, a first query maybe received that is directed to data set(s), in various embodiments. Thefirst query may be received that is directed to data set(s) separatelystored in remote data stores, in various embodiments. For example, aquery may be received via the various types of interfaces describedabove with regard to FIG. 4 (programmatic, user console, driver, etc.),in one embodiment. A query may be formatted according to different querylanguages, or specifications of query languages including StructuredQuery Language (SQL) and/Hive Query Language (HQL). The query mayinclude execution hints, specifying the type of query execution engineto utilize, query execution limits, or other parameters or propertiesfor configuring the execution of the query, in some embodiments.

As indicated at 1020, the first query may be evaluated with respect toresource configurations for executing the first query, in someembodiments. For example, a determination may be made as to whether thefirst query is the same or similar to prior query (e.g., by comparingquery language or data sources). If so, then the resourceconfiguration(s) of the same or prior queries may be evaluated accordingto a desired performance outcome or limitation (as discussed above). Ifno similar queries can be found, then a default computing resourceconfiguration may be selected or further types of similarity analysis orclassification may be performed (e.g., using a historical query model asdiscussed above and below). As indicated at 1030, a computing resourcemay be selected to execute the query from a plurality of differentlyconfigured computing resources that execute queries, based at least inpart, on the resource configuration, in some embodiments. The pluralityof computing resources may be pre-configured according to query enginesof different types, different configuration settings, and/or differentsizes (e.g., number of nodes or slots in a cluster). The determinedresource configuration may be compared with the configured computingresources to determine a best match or other similarity score for thedetermine resource configurations and the configuration of resources. Insome embodiments, selection of a computing resource may includeselection of a pool of resources (of a same configuration), so that theactual resource selected is determined from the pool.

As indicated at 1040, the first query may be routed to the selectedcomputing resource(s) for execution, in some embodiments. As indicatedat 1050, a result for the query generated at the selected computingresource(s) may be provided, in some embodiments. For example, theresults can be sent to a destination or location specified for the queryresults (e.g., in a client request), in one embodiment. The results maybe streamed back or aggregated (e.g., in a data store, like data storageservice 230) and provided as a batch (or batches, such as paginatedresults) via a same interface (e.g., programmatic, graphical, driver,console, etc.) that received the query.

As indicated at 1060, a query execution model according to executiondata for executing the query at the resources, in some embodiments. Forexample, the memory consumed, processing capacity consumed, number ofslots, nodes, or other execution units consumed/utilized, executionplans for prior queries (e.g., including the various types of operationsselected by the plan to perform the prior queries), the total executiontime to perform the query, a resource or determined cost (e.g., in termsof work or cost units, such as IOPS or monetary units), failure orsuccess indications, and/or failure types (e.g., including error codesor exceptions generated) may be included as part of execution data. Asupervised learning technique may take the execution data as part of thetraining set, mapping the performance or results of the query'sexecution to the features of the query (e.g., query execution plan,source data sets, etc.) in order to train the query execution model toachieve a similar outcome for the query if a similar query is received.

FIG. 11 is a high-level flowchart illustrating various methods andtechniques to apply a query execution model to select a resourceconfiguration, according to some embodiments. As indicated at 1110, afirst query directed to data set(s) may be received. The first query maybe received that is directed to data set(s) separately stored in remotedata stores, in various embodiments. For example, a query may bereceived via the various types of interfaces described above with regardto FIG. 4 (programmatic, user console, driver, etc.), in one embodiment.A query may be formatted according to different query languages, orspecifications of query languages including Structured Query Language(SQL) and/Hive Query Language (HQL). The query may include executionhints, specifying the type of query execution engine to utilize, queryexecution limits, or other parameters or properties for configuring theexecution of the query, in some embodiments.

As indicated at 1120, query execution plan(s) for the first query forquery engine(s) may be generated, in some embodiments. For example,query plan generation techniques that parse a SQL query, identify theSQL operations, and data identified in the query, determine theoperations to perform in order to accomplish the query, and assemble theoperations in multiple candidate execution plans (e.g., based on tablestatistics or other metadata for queried data sets) may be performed.Then the candidate execution plan based on a lowest cost to execute theplan may be selected so that optimized query execution plans aregenerated for the query, in some embodiments. If multiple query enginesmay be considered to execute the query, then multiple query executionplans may be generated, in some embodiments, one for each type of queryengine.

As indicated at 1130, historical query execution model(s) may be appliedto the first query and the query execution plan(s) for one of the queryexecution(s) for the query according to an execution limitation for thequery, in some embodiments. For example, feature vectors or otherrepresentations of the query and query plan(s) may be generated in orderto analyze the received query with respect to the historical querymodel. A classification or regression function determined from thehistorical query execution model(s) may be applied, in some embodimentsto generate similarity scores between a query and different classes ofqueries that may be described in the historical query execution model(e.g., short-running, long-running, compute intensive, memory intensive,DDL query, or DML query). The execution limitation may be applied todetermine which classifications do (or do not) satisfy the executionlimitation (e.g., a time limit for performing the query or cost limitfor performing the query), in some embodiments. Alternatively, differentclassifications may be mapped to different execution limitations (e.g.,classification A satisfies limit A, classification B satisfies limit B,etc.).

As indicated at 1140, a computing resource may be selected fromavailable computing resources according to the similarity of theavailable computing resources to the determined configuration. Forexample, a set (e.g., a snapshot of) available computing resources forexecuting the query may be obtained (e.g., by examining availabilitydata, resource pool data, pinging resources to see if they areavailable, or by requesting the set of available computing resourcesfrom a service like resource management service 290 in FIG. 2 above).The determined resource configuration may be then be compared with theavailable computing resources. For example, the number nodes in acluster resource may be evaluated to see if the number of nodes meets orexceeds the number of nodes identified in the resource configuration, inone embodiment, or whether the type of query engine matches thespecified type of query engine in the resource configuration. In someembodiments, configuration settings for the computing resource (e.g.,for the query engine) may be specified as part of the resourceconfiguration.

FIG. 12 is a high-level flowchart illustrating various methods andtechniques to determine resource provisioning recommendations accordingto the execution of prior queries, according to some embodiments. Asindicated at 1210, an event may be detected to provision a pool ofcomputing resources, in various embodiments. For example, provisioningevents may be triggered based on the time of day, day of the week,month, or year, or any other time-related data, workload or demand uponcomputing resources in existing pools, projected workload or demand forresource pools, or in response to a request (e.g., from a systemadministrator or a network-based service, such as managed query service270) to provision a resource pool.

If a provisioning event is detected, then a resource provisioning modelgenerated from prior executed queries to determine computing resource(s)to provision may be evaluated, as indicated at 1230, in variousembodiments. For example, the resource provisioning model may beevaluated with respect to one type of resource configuration criteria(e.g., number of nodes in a cluster) in order to determine otherresource configuration criteria (e.g., engine type and/or engineconfiguration settings). Other variables such as the time of day,overall state of resource pool(s), or other information that may alter aprovisioning recommendation (e.g., including excluding some resource(s)from configuration) may be used to evaluate the resource provisioningmodel. In some embodiments, resources to be provisioned may bedetermined according to general classifications, such as small, medium,or large clusters, or may be determined with a specific number of nodes,engine type and engine configuration settings. In some embodiments, atime-based analysis of the execution of prior queries and resourceconfigurations may be performed (e.g., examining demand and resourceconfiguration as a time series) to predict the demand, and thus numberand configuration of resources to include the pool based on a time ortime period associated with the provisioning event. For instance, aprovisioning event to launch a resource pool at 6:00 PM EST may evaluatethe demand for resources starting at 6:00 PM EST, as well as theconfiguration of the resources used to execute the queries received inorder to provision a number of resources that can satisfy a predicteddemand for the pool starting at 6:00 PM EST.

The determined resource(s) may then be provisioned, as indicated at1230, in some embodiments. For example, requests to other network-basedsystems or services to launch, create, instantiate, or configure newresources according to the configuration of the determined computingresources.

In some embodiments, pools of computing resource(s) configured for queryexecution may be monitored, in various embodiments, to provisioningindividual resources within a pool of computing resources. For example,as resources are leased, assigned, or otherwise allocated to executequeries, the number of available resources in the pool may decrease.Similarly, resource failures or resource expirations may reduce thenumber of available resources. If, for example, the number of availableresources falls below a maintenance threshold, then a provisioning eventmay be detected for an existing pool. Similar techniques to thosedescribed above may be performed to provide a recommendation as to thenumber and/or configuration of resources to provision for the existingpool.

The methods described herein may in various embodiments be implementedby any combination of hardware and software. For example, in oneembodiment, the methods may be implemented by a computer system (e.g., acomputer system as in FIG. 15) that includes one or more processorsexecuting program instructions stored on a computer-readable storagemedium coupled to the processors. The program instructions may beconfigured to implement the functionality described herein (e.g., thefunctionality of various servers and other components that implement thenetwork-based virtual computing resource provider described herein). Thevarious methods as illustrated in the figures and described hereinrepresent example embodiments of methods. The order of any method may bechanged, and various elements may be added, reordered, combined,omitted, modified, etc.

FIG. 13 is a logical block diagram that shows an illustrative operatingenvironment that includes a service provider network that can implementaspects of the functionality described herein, according to someembodiments. As discussed above, the service provider network 200 canprovide computing resources, like VM instances and storage, on apermanent or an as-needed basis. Among other types of functionality, thecomputing resources provided by the service provider network 200 can beutilized to implement the various services described above. As alsodiscussed above, the computing resources provided by the serviceprovider network 200 can include various types of computing resources,such as data processing resources like VM instances, data storageresources, networking resources, data communication resources, networkservices, and the like.

Each type of computing resource provided by the service provider network200 can be general-purpose or can be available in a number of specificconfigurations. For example, data processing resources can be availableas physical computers or VM instances in a number of differentconfigurations. The VM instances can execute applications, including webservers, application servers, media servers, database servers, some orall of the services described above, and/or other types of programs. TheVM instances can also be configured into computing clusters in themanner described above. Data storage resources can include file storagedevices, block storage devices, and the like. The service providernetwork 200 can also provide other types of computing resources notmentioned specifically herein.

The computing resources provided by the service provider network maybeimplemented, in some embodiments, by one or more data centers1304A-1304N (which might be referred to herein singularly as “a datacenter 1304” or in the plural as “the data centers 1304”). The datacenters 1304 are facilities utilized to house and operate computersystems and associated components. The data centers 1304 typicallyinclude redundant and backup power, communications, cooling, andsecurity systems. The data centers 1304 can also be located ingeographically disparate locations. One illustrative configuration for adata center 1304 that can be utilized to implement the technologiesdisclosed herein will be described below with regard to FIG. 14.

The customers and other users of the service provider network 200 canaccess the computing resources provided by the service provider network200 over a network 1302, which can be a wide area communication network(“WAN”), such as the Internet, an intranet or an Internet serviceprovider (“ISP”) network or a combination of such networks. For example,and without limitation, a computing device 1300 operated by a customeror other user of the service provider network 200 can be utilized toaccess the service provider network 200 by way of the network 1302. Itshould be appreciated that a local-area network (“LAN”), the Internet,or any other networking topology known in the art that connects the datacenters 1304 to remote customers and other users can be utilized. Itshould also be appreciated that combinations of such networks can alsobe utilized.

FIG. 14 is a logical block diagram illustrating a configuration for adata center that can be utilized to implement aspects of thetechnologies disclosed herein, according to various embodiments. is acomputing system diagram that illustrates one configuration for a datacenter 1304 that implements aspects of the technologies disclosed hereinfor providing managed query execution, such as managed query executionservice 270, in some embodiments. The example data center 1304 shown inFIG. 14 includes several server computers 1402A-1402F (which might bereferred to herein singularly as “a server computer 1402” or in theplural as “the server computers 1402”) for providing computing resources1404A-1404E.

The server computers 1402 can be standard tower, rack-mount, or bladeserver computers configured appropriately for providing the computingresources described herein (illustrated in FIG. 14 as the computingresources 1404A-1404E). As mentioned above, the computing resourcesprovided by the provider network 200 can be data processing resourcessuch as VM instances or hardware computing systems, computing clusters,data storage resources, database resources, networking resources, andothers. Some of the servers 1402 can also execute a resource manager1406 capable of instantiating and/or managing the computing resources.In the case of VM instances, for example, the resource manager 1406 canbe a hypervisor or another type of program may enable the execution ofmultiple VM instances on a single server computer 1402. Server computers1402 in the data center 1304 can also provide network services and othertypes of services, some of which are described in detail above withregard to FIG. 2.

The data center 1304 shown in FIG. 14 also includes a server computer1402F that can execute some or all of the software components describedabove. For example, and without limitation, the server computer 1402Fcan execute various components for providing different services of aprovider network 200, such as the managed query service 270, the datacatalog service 280, resource management service 290, and other services1410 (e.g., discussed above) and/or the other software componentsdescribed above. The server computer 1402F can also execute othercomponents and/or to store data for providing some or all of thefunctionality described herein. In this regard, it should be appreciatedthat the services illustrated in FIG. 14 as executing on the servercomputer 1402F can execute on many other physical or virtual servers inthe data centers 1304 in various configurations.

In the example data center 1304 shown in FIG. 14, an appropriate LAN1406 is also utilized to interconnect the server computers 1402A-1402F.The LAN 1406 is also connected to the network 1302 illustrated in FIG.13. It should be appreciated that the configuration and network topologydescribed herein has been greatly simplified and that many morecomputing systems, software components, networks, and networking devicescan be utilized to interconnect the various computing systems disclosedherein and to provide the functionality described above. Appropriateload balancing devices or other types of network infrastructurecomponents can also be utilized for balancing a load between each of thedata centers 1304A-1304N, between each of the server computers1402A-1402F in each data center 1304, and, potentially, betweencomputing resources in each of the data centers 1304. It should beappreciated that the configuration of the data center 1304 describedwith reference to FIG. 14 is merely illustrative and that otherimplementations can be utilized.

Embodiments of a managed query execution as described herein may beexecuted on one or more computer systems, which may interact withvarious other devices. One such computer system is illustrated by FIG.15. In different embodiments, computer system 2000 may be any of varioustypes of devices, including, but not limited to, a personal computersystem, desktop computer, laptop, notebook, or netbook computer,mainframe computer system, handheld computer, workstation, networkcomputer, a camera, a set top box, a mobile device, a consumer device,video game console, handheld video game device, application server,storage device, a peripheral device such as a switch, modem, router, orin general any type of computing device, computing node, compute node,computing system compute system, or electronic device.

In the illustrated embodiment, computer system 2000 includes one or moreprocessors 2010 coupled to a system memory 2020 via an input/output(I/O) interface 2030. Computer system 2000 further includes a networkinterface 2040 coupled to I/O interface 2030, and one or moreinput/output devices 2050, such as cursor control device 2060, keyboard2070, and display(s) 2080. Display(s) 2080 may include standard computermonitor(s) and/or other display systems, technologies or devices. In atleast some implementations, the input/output devices 2050 may alsoinclude a touch- or multi-touch enabled device such as a pad or tabletvia which a user enters input via a stylus-type device and/or one ormore digits. In some embodiments, it is contemplated that embodimentsmay be implemented using a single instance of computer system 2000,while in other embodiments multiple such systems, or multiple nodesmaking up computer system 2000, may host different portions or instancesof embodiments. For example, in one embodiment some elements may beimplemented via one or more nodes of computer system 2000 that aredistinct from those nodes implementing other elements.

In various embodiments, computer system 2000 may be a uniprocessorsystem including one processor 2010, or a multiprocessor systemincluding several processors 2010 (e.g., two, four, eight, or anothersuitable number). Processors 2010 may be any suitable processor capableof executing instructions. For example, in various embodiments,processors 2010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 2010 may commonly,but not necessarily, implement the same ISA.

In some embodiments, at least one processor 2010 may be a graphicsprocessing unit. A graphics processing unit or GPU may be considered adedicated graphics-rendering device for a personal computer,workstation, game console or other computing or electronic device.Modern GPUs may be very efficient at manipulating and displayingcomputer graphics, and their highly parallel structure may make themmore effective than typical CPUs for a range of complex graphicalalgorithms. For example, a graphics processor may implement a number ofgraphics primitive operations in a way that makes executing them muchfaster than drawing directly to the screen with a host centralprocessing unit (CPU). In various embodiments, graphics rendering may,at least in part, be implemented by program instructions configured forexecution on one of, or parallel execution on two or more of, such GPUs.The GPU(s) may implement one or more application programmer interfaces(APIs) that permit programmers to invoke the functionality of theGPU(s). Suitable GPUs may be commercially available from vendors such asNVIDIA Corporation, ATI Technologies (AMD), and others.

System memory 2020 may store program instructions and/or data accessibleby processor 2010. In various embodiments, system memory 2020 may beimplemented using any suitable memory technology, such as static randomaccess memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions and data implementingdesired functions, such as those described above are shown stored withinsystem memory 2020 as program instructions 2025 and data storage 2035,respectively. In other embodiments, program instructions and/or data maybe received, sent or stored upon different types of computer-accessiblemedia or on similar media separate from system memory 2020 or computersystem 2000. Generally speaking, a non-transitory, computer-readablestorage medium may include storage media or memory media such asmagnetic or optical media, e.g., disk or CD/DVD-ROM coupled to computersystem 2000 via I/O interface 2030. Program instructions and data storedvia a computer-readable medium may be transmitted by transmission mediaor signals such as electrical, electromagnetic, or digital signals,which may be conveyed via a communication medium such as a networkand/or a wireless link, such as may be implemented via network interface2040.

In one embodiment, I/O interface 2030 may coordinate I/O traffic betweenprocessor 2010, system memory 2020, and any peripheral devices in thedevice, including network interface 2040 or other peripheral interfaces,such as input/output devices 2050. In some embodiments, I/O interface2030 may perform any necessary protocol, timing or other datatransformations to convert data signals from one component (e.g., systemmemory 2020) into a format suitable for use by another component (e.g.,processor 2010). In some embodiments, I/O interface 2030 may includesupport for devices attached through various types of peripheral buses,such as a variant of the Peripheral Component Interconnect (PCI) busstandard or the Universal Serial Bus (USB) standard, for example. Insome embodiments, the function of I/O interface 2030 may be split intotwo or more separate components, such as a north bridge and a southbridge, for example. In addition, in some embodiments some or all of thefunctionality of I/O interface 2030, such as an interface to systemmemory 2020, may be incorporated directly into processor 2010.

Network interface 2040 may allow data to be exchanged between computersystem 2000 and other devices attached to a network, such as othercomputer systems, or between nodes of computer system 2000. In variousembodiments, network interface 2040 may support communication via wiredor wireless general data networks, such as any suitable type of Ethernetnetwork, for example; via telecommunications/telephony networks such asanalog voice networks or digital fiber communications networks; viastorage area networks such as Fibre Channel SANs, or via any othersuitable type of network and/or protocol.

Input/output devices 2050 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or retrieving data by one or more computer system 2000.Multiple input/output devices 2050 may be present in computer system2000 or may be distributed on various nodes of computer system 2000. Insome embodiments, similar input/output devices may be separate fromcomputer system 2000 and may interact with one or more nodes of computersystem 2000 through a wired or wireless connection, such as over networkinterface 2040.

As shown in FIG. 15, memory 2020 may include program instructions 2025,may implement the various methods and techniques as described herein,and data storage 2035, comprising various data accessible by programinstructions 2025. In one embodiment, program instructions 2025 mayinclude software elements of embodiments as described herein and asillustrated in the Figures. Data storage 2035 may include data that maybe used in embodiments. In other embodiments, other or differentsoftware elements and data may be included.

Those skilled in the art will appreciate that computer system 2000 ismerely illustrative and is not intended to limit the scope of thetechniques as described herein. In particular, the computer system anddevices may include any combination of hardware or software that canperform the indicated functions, including a computer, personal computersystem, desktop computer, laptop, notebook, or netbook computer,mainframe computer system, handheld computer, workstation, networkcomputer, a camera, a set top box, a mobile device, network device,internet appliance, PDA, wireless phones, pagers, a consumer device,video game console, handheld video game device, application server,storage device, a peripheral device such as a switch, modem, router, orin general any type of computing or electronic device. Computer system2000 may also be connected to other devices that are not illustrated, orinstead may operate as a stand-alone system. In addition, thefunctionality provided by the illustrated components may in someembodiments be combined in fewer components or distributed in additionalcomponents. Similarly, in some embodiments, the functionality of some ofthe illustrated components may not be provided and/or other additionalfunctionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a non-transitory,computer-accessible medium separate from computer system 2000 may betransmitted to computer system 2000 via transmission media or signalssuch as electrical, electromagnetic, or digital signals, conveyed via acommunication medium such as a network and/or a wireless link. Variousembodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Accordingly, the presentinvention may be practiced with other computer system configurations.

It is noted that any of the distributed system embodiments describedherein, or any of their components, may be implemented as one or moreweb services. For example, leader nodes within a data warehouse systemmay present data storage services and/or database services to clients asnetwork-based services. In some embodiments, a network-based service maybe implemented by a software and/or hardware system designed to supportinteroperable machine-to-machine interaction over a network. Anetwork-based service may have an interface described in amachine-processable format, such as the Web Services DescriptionLanguage (WSDL). Other systems may interact with the web service in amanner prescribed by the description of the network-based service'sinterface. For example, the network-based service may define variousoperations that other systems may invoke, and may define a particularapplication programming interface (API) to which other systems may beexpected to conform when requesting the various operations.

In various embodiments, a network-based service may be requested orinvoked through the use of a message that includes parameters and/ordata associated with the network-based services request. Such a messagemay be formatted according to a particular markup language such asExtensible Markup Language (XML), and/or may be encapsulated using aprotocol such as Simple Object Access Protocol (SOAP). To perform a webservices request, a network-based services client may assemble a messageincluding the request and convey the message to an addressable endpoint(e.g., a Uniform Resource Locator (URL)) corresponding to the webservice, using an Internet-based application layer transfer protocolsuch as Hypertext Transfer Protocol (HTTP).

In some embodiments, web services may be implemented usingRepresentational State Transfer (“RESTful”) techniques rather thanmessage-based techniques. For example, a web service implementedaccording to a RESTful technique may be invoked through parametersincluded within an HTTP method such as PUT, GET, or DELETE, rather thanencapsulated within a SOAP message.

The various methods as illustrated in the FIGS. and described hereinrepresent example embodiments of methods. The methods may be implementedin software, hardware, or a combination thereof. The order of method maybe changed, and various elements may be added, reordered, combined,omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended that the invention embrace all such modifications and changesand, accordingly, the above description to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A system, comprising: a memory to store programinstructions which, if performed by at least one processor, cause the atleast one processor to perform a method to at least: receive a firstquery directed to one or more data sets; in response to the receipt ofthe first query: evaluate the first query with respect to a history ofresource configurations that executed prior queries to determine aresource configuration for executing the first query comprising:generate an execution plan for at least one query engine; and apply ahistorical query execution model to the first query and the at least onequery plan to determine the resource configuration for executing thefirst query; select a computing resource to execute the first query froma plurality of differently configured computing resources that executequeries, based, at least in part, on the resource configuration; androute the first query to the selected computing resource for execution.2. The system of claim 1, wherein the first query indicates an executionlimit for the first query, and wherein the selection of the computingresource to execute the first query determines that the computingresource is able to satisfy the execution limit for the first query. 3.The system of claim 1, wherein the memory and the at least one processorare implemented as part of a managed query service, wherein the one ormore data sets are stored as part of a data storage service, wherein thedata storage service and the managed query service are implemented aspart of a provider network, wherein the first query is one of aplurality of different queries received at the managed query servicefrom different clients of the managed query service, wherein theevaluating, the selecting, and the routing are performed for thedifferent queries, and wherein the method further comprises update thehistorical query execution model based on respective execution data forthe different queries.
 4. A method, comprising: receiving a first querydirected to one or more data sets; in response to receiving the firstquery: evaluating the first query with respect to a history of resourceconfigurations that executed prior queries to determine a resourceconfiguration for executing the first query, comprising: generating anexecution plan for a query engine; and applying a historical queryexecution model to the first query and the query plan to determine theresource configuration for executing the first query; selecting one ormore computing resources to execute the first query from a plurality ofdifferently configured computing resources that execute queries, based,at least in part, on the resource configuration; and performing thefirst query at the selected computing resources with respect to the oneor more data sets.
 5. The method of claim 4, wherein the selecting ofthe computing resource to execute the first query comprises determiningthat the computing resource satisfies the execution limit for the firstquery.
 6. The method of claim 4, further comprising updating thehistorical query execution model based on execution data generated fromthe execution of the first query at the selected computing resources. 7.The method of claim 6, further comprising updating the historical queryexecution model based on metadata describing the one or more data sets.8. The method of claim 4, wherein determining the resource configurationfor the computing resources comprises determining configuration settingsfor a query engine to execute the first query.
 9. The method of claim 4,wherein determining the resource configuration for the computingresources comprises selecting one query engine from a plurality of queryengines to execute the first query.
 10. The method of claim 4, whereinselecting the one or more computing resources to execute the first querycomprises including capacity to execute one or more subsequent queriesat the selected computing resources based, at least in part, on aconcurrent query pattern for a submitter of the first query.
 11. Themethod of claim 4, further comprising: prior to receiving the firstquery: evaluating a resource provisioning model generated from priorexecuted queries to determine at least one computing resource toprovision, including the one or more computing resources; andprovisioning the determined at least one computing resource forexecuting queries.
 12. A non-transitory, computer-readable storagemedium, storing program instructions that when executed by one or morecomputing devices cause the one or more computing devices to implement:receiving a first query directed to one or more data sets; in responseto the receipt of the first query: evaluating the first query withrespect to a history of resource configurations that executed priorqueries to determine a resource configuration for executing the firstquery, comprising: generating an execution plan for a query engine; andapplying a historical query execution model to the first query and thequery plan to determine the resource configuration for executing thefirst query; selecting a computing resource to execute the first queryfrom a plurality of differently configured computing resources thatexecute queries, based, at least in part, on the resource configuration;and performing the first query at the selected computing resource. 13.The non-transitory, computer-readable storage medium of claim 12,wherein the first query indicates an execution limit for the firstquery, and wherein the selection of the computing resource to executethe first query determines that the computing resource is able tosatisfy the execution limit for the first query.
 14. The non-transitory,computer-readable storage medium of claim 3, wherein determining theresource configuration for executing the first query comprisesdetermining a number of slots included in the computing resources. 15.The non-transitory, computer-readable storage medium of claim 12,wherein the program instructions cause the one or more computing devicesto implement: prior to receiving the first query: detecting aprovisioning event to provision a pool of computing resources thatincludes the one or more computing resources; evaluating a resourceprovisioning model generated from prior executed queries to determine atleast one computing resource to provision for the pool, including theone or more computing resources; and provisioning the determinedcomputing resources for the pool.
 16. The non-transitory,computer-readable storage medium of claim 15, wherein the programinstructions cause the one or more computing devices to furtherimplement updating the resource provisioning model based on executiondata generated from the execution of the first query at the selectedcomputing resources.
 17. The non-transitory, computer-readable storagemedium of claim 15, wherein evaluating the resource provisioning modelgenerated from prior executed queries to determine the at least onecomputing resource to provision comprises performing a time-basedevaluation of the prior executed queries to determine the at least onecomputing resource for a time period associated with the provisioningevent.